Producing alternative segmentations of data into blocks in a data deduplication system

ABSTRACT

For producing secondary segmentations of data into blocks and corresponding digests for input data in a data deduplication system using a processor device in a computing environment, digests are calculated for an input data chunk using a primary segmentation into blocks. Secondary segmentations are produced for each of the data mismatches based on reference data, and used to calculate further data matches. The primary segmentation and the corresponding primary digests are stored for the input data chunk.

CROSS REFERENCE TO RELATED APPLICATIONS

The present application, listed as docket number TUC9-2013-0100US1, iscross-related to the following seventeen applications each listed as:docket number TUC9-2013-0058US1, docket number TUC9-2013-0095US1, docketnumber TUC9-2013-0046US1, docket number TUC9-2013-0096US1, docket numberTUC9-2013-0097US1, docket number TUC9-2013-0098US1, docket numberTUC9-2013-0059US1, docket number TUC9-2013-0099US1, docket numberTUC9-2013-0060US1, docket number TUC9-2013-0061US1, docket numberTUC9-2013-0062US1, docket number TUC9-2013-0074US1, docket numberTUC9-2013-0091US1, docket number TUC9-2013-0101US1, docket numberTUC9-2013-0114US1, docket number TUC9-2013-0115US1, and docket numberTUC9-2013-0116US1 all of which are filed on the same day as the presentinvention and the entire contents of which are incorporated herein byreference and are relied upon for claiming the benefit of priority.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates in general to computers, and moreparticularly to producing alternative segmentations of data into blocksin a data deduplication system in a computing environment.

2. Description of the Related Art

In today's society, computer systems are commonplace. Computer systemsmay be found in the workplace, at home, or at school. Computer systemsmay include data storage systems, or disk storage systems, to processand store data. Large amounts of data have to be processed daily and thecurrent trend suggests that these amounts will continue beingever-increasing in the foreseeable future. An efficient way to alleviatethe problem is by using deduplication. The idea underlying adeduplication system is to exploit the fact that large parts of theavailable data are copied again and again, by locating repeated data andstoring only its first occurrence. Subsequent copies are replaced withpointers to the stored occurrence, which significantly reduces thestorage requirements if the data is indeed repetitive.

SUMMARY OF THE DESCRIBED EMBODIMENTS

In one embodiment, a method is provided for producing a multiplicity ofsecondary segmentations of data into blocks and corresponding digestsfor input data in a data deduplication system using a processor devicein a computing environment. In one embodiment, by way of example only,digests are calculated for an input data chunk using a primarysegmentation into blocks. Secondary segmentations are produced for eachof the data mismatches based on reference data, and used to calculatefurther data matches. The primary segmentation and the correspondingprimary digests are stored for the input data chunk.

In another embodiment, a computer system is provided for producing amultiplicity of secondary segmentations of data into blocks andcorresponding digests for input data in a data deduplication systemusing a processor device, in a computing environment. The computersystem includes a computer-readable medium and a processor in operablecommunication with the computer-readable medium. In one embodiment, byway of example only, the processor, calculates digests for an input datachunk using a primary segmentation into blocks. Secondary segmentationsare produced for each of the data mismatches based on reference data,and used to calculate further data matches. The primary segmentation andthe corresponding primary digests are stored for the input data chunk.

In a further embodiment, a computer program product is provided forproducing a multiplicity of secondary segmentations of data into blocksand corresponding digests for input data in a data deduplication systemusing a processor device, in a computing environment. Thecomputer-readable storage medium has computer-readable program codeportions stored thereon. The computer-readable program code portionsinclude a first executable portion that calculates digests for an inputdata chunk using a primary segmentation into blocks. Secondarysegmentations are produced for each of the data mismatches based onreference data, and used to calculate further data matches. The primarysegmentation and the corresponding primary digests are stored for theinput data chunk.

In addition to the foregoing exemplary method embodiment, otherexemplary system and computer product embodiments are provided andsupply related advantages. The foregoing summary has been provided tointroduce a selection of concepts in a simplified form that are furtherdescribed below in the Detailed Description. This Summary is notintended to identify key features or essential features of the claimedsubject matter, nor is it intended to be used as an aid in determiningthe scope of the claimed subject matter. The claimed subject matter isnot limited to implementations that solve any or all disadvantages notedin the background.

BRIEF DESCRIPTION OF THE DRAWINGS

In order that the advantages of the invention will be readilyunderstood, a more particular description of the invention brieflydescribed above will be rendered by reference to specific embodimentsthat are illustrated in the appended drawings. Understanding that thesedrawings depict embodiments of the invention and are not therefore to beconsidered to be limiting of its scope, the invention will be describedand explained with additional specificity and detail through the use ofthe accompanying drawings, in which:

FIG. 1 is a block diagram illustrating a computing system environmenthaving an example storage device in which aspects of the presentinvention may be realized;

FIG. 2 is a block diagram illustrating a hardware structure of datastorage system in a computer system in which aspects of the presentinvention may be realized;

FIG. 3 is a flowchart illustrating an exemplary method for digestretrieval based on similarity search in deduplication processing in adata deduplication system in which aspects of the present invention maybe realized;

FIG. 4 is a flowchart illustrating an exemplary alternative method fordigest retrieval based on similarity search in deduplication processingin a data deduplication system in which aspects of the present inventionmay be realized;

FIG. 5 is a flowchart illustrating an exemplary method for efficientcalculation of both similarity search values and boundaries of digestblocks using a single linear calculation of rolling hash values in adata deduplication system in which aspects of the present invention maybe realized;

FIG. 6 is a block diagram illustrating calculating rolling hash valuesin which aspects of the present invention may be realized;

FIG. 7 is a block diagram illustrating an exemplary structure forfinding additional identical data with secondary segmentation in whichaspects of the present invention may be realized;

FIG. 8 is a block diagram illustrating a method for finding additionalidentical data using a secondary segmentation in which aspects of thepresent invention may be realized; and

FIG. 9 is a flowchart illustrating an exemplary method for producing andusing secondary segmentations derived from references in a datadeduplication system in which aspects of the present invention may berealized.

DETAILED DESCRIPTION OF THE DRAWINGS

Data deduplication is a highly important and vibrant field in computingstorage systems. Data deduplication refers to the reduction and/orelimination of redundant data. In data deduplication, a data object,which may be a file, a data stream, or some other form of data, ispartitioned into one or more parts called chunks or blocks. In a datadeduplication process, duplicate copies of data are reduced oreliminated, leaving a minimal amount of redundant copies, or a singlecopy of the data, respectively. The goal of a data deduplication systemis to store a single copy of duplicated data, and the challenges inachieving this goal are efficiently finding the duplicate data patternsin a typically large repository, and storing the data patterns in astorage efficient deduplicated form. A significant challenge indeduplication storage systems is scaling to support very largerepositories of data. Such large repositories can reach sizes ofPetabytes (1 Petabyte=2⁵⁰ bytes) or more. Deduplication storage systemssupporting such repository sizes, must provide efficient processing forfinding duplicate data patterns within the repositories, whereefficiency is measured in resource consumption for achievingdeduplication (resources may be CPU cycles, RAM storage, persistentstorage, networking, etc.).

For example, in one embodiment, a hash-based deduplication processincludes the following operations: segmenting the input data intosegments; calculating a cryptographic hash value, denoted herewith as adigest, for each segment; and looking up the hash value in an index ofknown hash values. If the hash value is found, the data is alreadystored and does not need to be stored again, hence the deduplication.

In one embodiment, a variety of techniques may be used to segment astream of bytes based on the data itself. A segmenting process startswith a calculation of rolling hash values for the input data, producinga hash value for each window of bytes (also denoted as a seed) at eachbyte position in the data, as shown below in FIG. 6. If this hash valuematches some criterion or criteria, then a segment boundary is declared.More complex segmentation methods also take into account neighboringboundaries, the relation between them or their segment sizes, thehistory of segmentation, etc. The rolling hash values are calculatedbased on a sliding window of bytes. There is a trade-off with regards tothe size of the sliding window of bytes based on which the rolling hashvalues are calculated. If this sliding window is small, the rolling hashvalues are based on fewer bytes thus constituting a less reliable randomvariable, and also reducing the statistical significance of thesegmenting calculations which are based on these values. If the slidingwindow is large, then each rolling hash value is more sensitive tochanges in the data (namely, a given change in the data affects morehash values), thus making the segmenting positions more sensitive tochanges in the data, as depicted below in FIG. 7, thus allowing minor orotherwise localized changes in the input data to alter the segmentationin an undesirable way. In other words, whether the window size isincreased or decreased, both directions have a negative effect on thequality of the block segmentation. Considering the trade-off specifiedabove, a need exists to use additional information to facilitate andimprove the quality of block segmentation.

Thus, in one embodiment, a deduplication storage system may be based onmaintaining a search optimized index of values known as fingerprints ordigests, where a small fingerprint represents a larger block of data inthe repository. The fingerprint values may be cryptographic hash valuescalculated based on the blocks' data. In one embodiment, secure hashalgorithm (SHA), e.g. SHA-1 or SHA-256, which are a family ofcryptographic hash functions, may be used. Identifying fingerprintmatches, using index lookup, enables to store references to data thatalready exists in a repository. In one embodiment, determining segmentboundaries can be based on the data itself.

To provide reasonable deduplication in this approach, the mean size ofthe data blocks based on which fingerprints are generated must belimited to smaller sizes and may not be too large. The reason being thata change of a bit within a data block will probabilistically change thedata block's corresponding fingerprint, and thus having large datablocks makes the scheme more sensitive to updates in the data ascompared to having small blocks. A typical data block size may rangefrom 4 KB to 64 KB, depending on the type of application and workload.Thus, by way of example only, small data blocks may range in sizes of upto 64 KB, and large data blocks are those data blocks having a sizelarger than 64 KB.

To support very large repositories scaling to Petabytes (e.g.,repositories scaling to at least one Petabyte), the number offingerprints to store coupled with the size of a fingerprint (rangingbetween 16 bytes and 64 bytes), becomes prohibitive. For example, for 1Petabyte of deduplicated data, with a 4 KB mean data block size, and 32bytes fingerprint size (e.g. of SHA-256), the storage required to storethe fingerprints is 8 Terabytes. Maintaining a search optimized datastructure for such volumes of fingerprints is difficult, and requiresoptimization techniques. However existing optimization techniques do notscale to these sizes while maintaining performance. For this reason, toprovide reasonable performance, the supported repositories have to berelatively small (on the order of tens of TB). Even for such smallersizes, considerable challenges and run-time costs arise due to the largescale of the fingerprint indexes, that create a bottle-neck (e.g., chunklook disk bottleneck) in deduplication processing. A solid-state disk(SSD) to cache parts of the index in low persistent memory may be usedbut such is limited by the size and cost of the SSD.

To solve this problem, in one embodiment, a deduplication system may bebased on a two-step approach for searching data patterns duringdeduplication. In the first step, a large chunk of incoming data (e.g. afew megabytes) is searched in the repository for similar (rather thanidentical) data chunks of existing data, and the incoming data chunk ispartitioned accordingly into intervals and paired with corresponding(similar) repository intervals. In the second step, a byte-wise matchingalgorithm is applied on pairs of similar intervals, to identifyidentical sub-intervals, which are already stored in a repository ofdata. The matching algorithm of the second step relies on reading allthe relevant similar data in the repository in order to compare itbyte-wise to the input data.

Yet, a problem stemming from a byte-wise comparison of data underlyingthe matching algorithm of the second step, is that data of roughly thesame size and rate as the incoming data should be read from therepository, for comparison purposes. For example, a system processing 1GB of incoming data per second, should read about 1 GB of data persecond from the repository for byte-wise comparison. This requiressubstantially high capacities of I/O per second of the storage devicesstoring the repository data, which in turn increases their cost.

Additional trends in information technology coinciding with the aboveproblem are the following: (1) Improvements in the computing ability byincreasing CPU speeds and the number of CPU cores. (2) Increase in diskdensity, while disk throughput remains relatively constant or improvingonly modestly. This means that there are fewer spindles relative to thedata capacity, thus practically reducing the overall throughput. Due tothe problem specified above, there is a need to design an alternativesolution, to be integrated in a two step deduplication system embodimentspecified above, that does not require reading from the repository inhigh rates/volumes.

Therefore, in one embodiment, by way of example only, additionalembodiments address these problem, as well as shifts resourceconsumption from disks to the CPUs, to benefit from the above trends.The embodiments described herein are integrated within the two step andscalable deduplication embodiments embodiment described above, and usesa similarity search to focus lookup of digests during deduplication. Inone embodiment, a global similarity search is used as a basis forfocusing the similarity search for digests of repository data that ismost likely to match input data.

The embodiments described herein significantly reduce the capacity ofI/O per second required of underlying disks, benefit from the increasesin computing ability and in disk density, and considerably reduce thecosts of processing, as well as maintenance costs and environmentaloverhead (e.g. power consumption).

In one embodiment, input data is segmented into small segments (e.g. 4KB) and a digest (a cryptographic hash value, e.g. SHA1) is calculatedfor each such segment. First, a similarity search algorithm, asdescribed above, is applied on an input chunk of data (e.g. 16 MB), andthe positions of the most similar reference data in the repository arelocated and found. These positions are then used to lookup the digestsof the similar reference data. The digests of all the data contained inthe repository are stored and retrieved in a form that corresponds totheir occurrence in the data. Given a position of a section of datacontained in the repository, the digests associated with the section ofdata are efficiently located in the repository and retrieved. Next,these reference digests are loaded into memory, and instead of comparingdata to find matches, the input digests and the loaded reference digestsare matched.

The described embodiments provide a new fundamental approach forarchitecting a data deduplication system, which integrates a scalabletwo step approach of similarity search followed by a search of identicalmatching segments, with an efficient and cost effectivedigest/fingerprint based matching algorithm (instead of byte-wise datacomparison). The digest/fingerprint based matching algorithm enables toread only a small fraction (1%) of the volume of data required bybyte-wise data comparison. The present invention proposed herein, adeduplication system can provide high scalability to very large datarepositories, in addition to high efficiency and performance, andreduced costs of processing and hardware.

In one embodiment, by way of example only, the term “similar data” maybe referred to as: for any given input data, data which is similar tothe input data is defined as data which is mostly the same (i.e. notentirely but at least 50% similar) as the input data. From looking atthe data in a binary view (perspective), this means that similar data isdata where most (i.e. not entirely but at least 50% similar) of thebytes are the same as the input data.

In one embodiment, by way of example only, the term “similar search” maybe referred to as the process of searching for data which is similar toinput data in a repository of data. In one embodiment, this process maybe performed using a search structure of similarity elements, which ismaintained and searched within.

In one embodiment, by way of example only, the term “similarityelements” may be calculated based on the data and facilitate a globalsearch for data which is similar to input data in a repository of data.In general, one or more similarity elements are calculated, andrepresent, a large (e.g. at least 16 MB) chunk of data.

Thus, the various embodiments described herein provide various solutionsfor digest retrieval based on a similarity search in deduplicationprocessing in a data deduplication system using a processor device in acomputing environment. In one embodiment, by way of example only, inputdata is partitioned into fixed sized data chunks. Similarity elements,digest block boundaries and digest values are calculated for each of thefixed sized data chunks. Matching similarity elements are searched forin a search structure (i.e. index) containing the similarity elementsfor each of the fixed sized data chunks in a repository of data.Positions of similar data are located in a repository. The positions ofthe similar data are used to locate and load into the memory storeddigest values and corresponding stored digest block boundaries of thesimilar data in the repository. It should be noted that in oneembodiment the positions may be either physical or logical (i.e.virtual). The positions are of data inside a repository of data. Theimportant property of a ‘position’ is that given a position (physical orlogical) in the repository's data, the data in that position can beefficiently located and accessed. The digest values and thecorresponding digest block boundaries are matched with the stored digestvalues and the corresponding stored digest block boundaries to find datamatches.

Thus, the various embodiments described herein provide various solutionsfor digest retrieval based on a similarity search in deduplicationprocessing in a data deduplication system using a processor device in acomputing environment. In one embodiment, by way of example only, inputdata is partitioned into fixed sized data chunks. Similarity elements,digest block boundaries and digest values are calculated for each of thefixed sized data chunks. Matching similarity elements are searched forin a search structure (i.e. index) containing the similarity elementsfor each of the fixed sized data chunks in a repository of data.Positions of similar data are located in a repository. The positions ofthe similar data are used to locate and load into the memory storeddigest values and corresponding stored digest block boundaries of thesimilar data in the repository. The digest values and the correspondingdigest block boundaries are matched with the stored digest values andthe corresponding stored digest block boundaries to find data matches

In one embodiment, the present invention provides a solution forutilizing a similarity search to load into memory the relevant digestsfrom the repository, for efficient deduplication processing. In a datadeduplication system, deduplication is performed by partitioning thedata into large fixed sized chunks, and for each chunk calculating (2things—similarity elements and digest blocks/digest values) hash values(digest block/digest value) for similarity search and digest values. Thedata deduplication system searches for matching similarity values of thechunks in a search structure of similarity values, and finds thepositions of similar data in the repository. The data deduplicationsystem uses these positions of similar data to locate and load intomemory stored digests of the similar repository data, and matching inputand repository digest values to find data matches.

In one embodiment, the present invention provides for efficientcalculation of both similarity search values and segmentation (i.e.boundaries) of digest blocks using a single linear calculation ofrolling hash values. In a data deduplication system, the input data ispartitioned into chunks, and for each chunk a set of rolling hash valuesis calculated. A single linear scan of the rolling hash values producesboth similarity search values and boundaries of the digest blocks of thechunk. Each rolling hash value corresponds to a consecutive window ofbytes in byte offsets. The similarity search values are used to searchfor similar data in the repository. The digest blocks segmentation isused to calculate digest block boundaries and corresponding digestvalues of the chunk, for digests matching. Each rolling hash valuecontributes to the calculation of the similarity values and to thecalculation of the digest blocks segmentations. Each rolling hash valuemay be discarded after contributing to the calculations. The describedembodiment provides significant processing efficiency and reduction ofCPU consumption, as well as considerable performance improvement.

Thus, as described above, the deduplication approach of the presentinvention uses a two-step process for searching data patterns duringdeduplication. In the first step, a large chunk of incoming data (e.g.16 megabytes “MB”) is searched in the repository for similar (ratherthan identical) chunks of existing data, and the incoming chunk ispartitioned accordingly into intervals, and paired with corresponding(similar) repository intervals. The similarity search structure (or“index”) used in the first step is compact and simple to maintain searchwithin, because the elements used for a similarity search are verycompact relative to the data they represent (e.g. 16 bytes representing4 megabytes). Further included in the first step, in addition to acalculation of similarity elements, is a calculation of digest segmentsand respective digest values for the input chunk of data. All thesecalculations are based on a single calculation of rolling hash values.In the second step, reference digests of the similar repositoryintervals are retrieved, and then the input digests are matched with thereference digests, to identify data matches.

In one embodiment, in the similarity based deduplication approach asdescribed herein, a stream of input data is partitioned into chunks(e.g. at least 16 MB), and each chunk is processed in two main steps. Inthe first step a similarity search process is applied, and positions ofthe most similar reference data in the repository are found. Within thisstep both similarity search elements and digest segments boundaries arecalculated for the input chunk, based on a single linear calculation ofrolling hash values. Digest values are calculated for the input chunkbased on the produced segmentation, and stored in memory in the sequenceof their occurrence in the input data. The positions of similar data arethen used to lookup the digests of the similar reference data and loadthese digests into memory, also in a sequential form. Then, the inputdigests are matched with the reference digests to form data matches.

When deduplication of an input chunk of data is complete, the inputchunk of data's associated digests are stored in the repository, toserve as reference digests for subsequent input data. The digests arestored in a linear form, which is independent of the deduplicated formby which the data these digests describe is stored, and in the sequenceof their occurrence in the data. This method of storage enablesefficient retrieval of sections of digests, independent of fragmentationcharacterizing deduplicated storage forms, and thus low on IO andcomputational resource consumption.

In addition, to solve the bottleneck problem as described above, in oneembodiment, a deduplication system, as described herein, may use the twostep approach for searching data patterns during deduplication. In thefirst step, a large chunk of incoming data (e.g. a few megabytes) issearched in the repository for similar (rather than identical) datachunks of existing data, and the incoming data chunk is partitionedaccordingly into intervals and paired with corresponding (similar)repository intervals. In the second step, a byte-wise matching algorithmis applied on pairs of similar intervals, to identify identicalsub-intervals, which are already stored in a repository of data. Thesimilarity index used in this step is very compact and simple tomaintain and search within, since the elements used for similaritysearch are very compact relative to the data they represent (e.g. 16bytes representing 4 megabytes). Further included in the first step is acalculation of similarity characteristics as well as digest segments andrespective digest values, of the input chunk of data. These calculationsare based on a single calculation of rolling hash values. The matchingalgorithm of the second step relies on reading all the relevant similardata in the repository in order to compare it byte-wise to the inputdata. In the second step, reference digests of the similar repositoryintervals are retrieved, and then the input digests are matched with thereference digests, to identify data matches. This approach works verywell on data sets where the generations of data have a low to moderatechange rate (roughly up to 30% change rate) relative to previousgenerations. Such change rates are very typical for the most common usecases, and are specifically typical for data backup environments.

Thus, as described above, similarity search is used to find referencedigests that are most likely to match the digests of the input data. Thereference digests are retrieved in a sequence by which they occur in thereference data. This sequence includes, in addition to the digestvalues, the sizes of their respective segments. The digest look upoperations, as described above, provides the steps of the similaritysearch leading to retrieval of digests of the most similar data andcomparing the digests of the input data to the digests of the mostsimilar reference data. The steps of the similarity search provides aframework of identifying the context of the reference data which is mostsimilar to the input data, and then retrieving the digests of that mostsimilar reference data. These retrieved reference digests are in thesequence of their occurrence in the reference data.

Thus, the present invention enables to use the information of thesequence of occurrence of the reference digests to produce secondarysegmentations of the input data. The reference digest values and sizesof their respective segments are used by the present invention. Once asequence of matches between the input data and the reference data hasbeen found, the present invention considers each one of the mismatches,to reduce its size using the additional information of the positions andsizes of the reference segments, thus improving the effectiveness ofdeduplication.

Each mismatch may include data that was affected by localized changeswithin specific windows of bytes that have produced segment boundariesin previous versions of that data, thus causing a change in thesegmentation of the input data. By using the segmentation that is ineffect in the considered reference data, that is determined as similarto the input data by the similarity search procedure, and projectingthis segmentation onto the input data within a data mismatch, a set ofsecondary segmentations is produced for the input data within the datamismatch. A set of segmentations is produced because there may beseveral intervals of reference data which are determined to be similarto a given input data interval. FIG. 8 illustrates this. The digestvalues of each of these secondary segmentations are calculated andcompared to the digest values of the references. If any digest valuewithin a mismatch is matched based on the secondary segmentations, thenthe size of the mismatch is reduced and deduplication is increased.Namely, the secondary segmentations produced by the current inventionenable to overcome changes in the data that affect the rolling hashwindows that previously produced segment boundaries on similar data.

In one embodiment, after applying the similarity search, identifyingsimilar reference intervals, and calculating matches and mismatchesbased on the digest values of the input data and the reference data, thepresent invention provides a solution to finding additional matchingdata in each one of the mismatches produced by the previous steps.

The present invention considers each one of the mismatches. In oneembodiment, by way of example only, let Hv, Hr, Hs, Tv, Tr and Ts benumeric input parameters. For each mismatch the Hv digests covered by amatch immediately preceding the mismatch are processed, as specifiednext. Each of these Hv digests are searched in a digests searchstructure, producing Hr matching reference digests, each located at adifferent reference position. For each of these Hr matching referencedigests, its following Hs reference digest segments are projected bytheir positions and sizes onto the input data, producing new secondaryinput digest segments. In one embodiment, a same process is applied tothe Tv digests covered by a match immediately following the mismatch.These Tv input digests are matched with Tr matching reference digests.For each of these Tr matching reference digests, its preceding Tsreference digest segments are projected by their positions and sizesonto the input data, producing further new secondary input digestsegments.

For each of the new secondary digest segments, respective digest valuesare calculated based on the input data, thus producing a set of newsecondary digests for the mismatch being processed. Then, the newsecondary digests are matched with the reference digests (which arealready loaded into a search structure in memory), and each new matchfound is recorded as a new data identity, thus reducing the mismatchsize, and improving the overall deduplication ratio.

In one embodiment, the secondary segmentations produced for eachmismatch are used to find more matching data, and the segmentationeventually stored for the input data is its primary segmentation, tofacilitate deduplication with the next generations of this data. Thepresent invention provides an improvement over existing hash baseddeduplication approaches, by enabling to identify more unchanged data.

For example, in one embodiment, the present invention produces a set ofsecondary segmentations for each mismatch, based on referencesegmentations. In one embodiment, a data deduplication processcalculating digests on an input data chunk, producing data matches andmismatches based on matching of input digests with reference digests,and, for each mismatch, obtaining and applying segmentations fromsimilar reference intervals. In one embodiment, producing the datamatches and mismatches includes searching the input digests in a searchstructure of reference digests. The process for obtaining possiblereference segmentations includes consideration of digests included inmatches preceding and following the mismatch. The considered digests arematched with reference digests. In one embodiment, the matches serve asstarting position for segmentations which are projected onto the inputdata. New digest values are calculated on the input data based on thefound segmentations. The new digest values are searched in a searchstructure of reference digest values. Each new match found represents anew match of data.

In one embodiment, the present invention produces secondarysegmentations and digests, but stores the primary segmentation anddigests for the input data. In one embodiment, a data deduplicationprocess includes calculating digests on an input data chunk usingprimary segmentation, obtaining and applying secondary segmentations foreach mismatch based on reference data, and storing a primarysegmentation and digests for the input data. In one embodiment,producing the data matches and mismatches includes searching the inputdigests in a search structure of reference digests. The process forobtaining possible reference segmentations includes consideration ofdigests included in matches preceding and following the mismatch. Theconsidered digests are matched with reference digests. In oneembodiment, the matches serve as starting position for segmentationswhich are projected onto the input data. New digest values arecalculated on the input data based on the found segmentations. The newdigest values are searched in a search structure of reference digestvalues. Each new match found represents a new match of data.

In one embodiment, rather than considering only the input data fordetermining an appropriate segment size, the present invention considersreference data in a repository of data, which was determined as similarto the input data, to derive alternative segmentations and project thesesegmentations onto the input data.

In one embodiment, the present invention finds additional repositorydigests that match input digests, which were already matched withrepository digests, and from these additional matching repositorydigests, it derives alternative segmentations and projects thesesegmentation onto the input data. The present invention has a uniquefeature of positioning repository segments and respective digests withina context of adjacent repository segments and respective digests, whichenables to project segmentations from repository data sections onto theinput data.

In one embodiment, alternative segmentations to be applied on the inputdata are not pre-determined and not arbitrary, but rather are derivedfrom the segmentations of repository data that was determined as similarto the input data. Essentially, the present invention locates additionalrepository digests that match input digests, which were already matchedwith repository digests, and from these additional matching repositorydigests, it derives alternative segmentations and projects thesesegmentation onto the input data.

In one embodiment, since the new segmentations are derived fromrepository data that is determined to be similar to the input data, theprobability that the additional segmentations will enable to findadditional data matches and improve deduplication is considerably high.For example, in one embodiment, the present invention generatesadditional segmentations for the input data, derived from segmentationsof similar repository data, and projects these additional segmentationsonto the input data to improve deduplication.

Turning now to FIG. 1, exemplary architecture 10 of a computing systemenvironment is depicted. The computer system 10 includes centralprocessing unit (CPU) 12, which is connected to communication port 18and memory device 16. The communication port 18 is in communication witha communication network 20. The communication network 20 and storagenetwork may be configured to be in communication with server (hosts) 24and storage systems, which may include storage devices 14. The storagesystems may include hard disk drive (HDD) devices, solid-state devices(SSD) etc., which may be configured in a redundant array of independentdisks (RAID). The operations as described below may be executed onstorage device(s) 14, located in system 10 or elsewhere and may havemultiple memory devices 16 working independently and/or in conjunctionwith other CPU devices 12. Memory device 16 may include such memory aselectrically erasable programmable read only memory (EEPROM) or a hostof related devices. Memory device 16 and storage devices 14 areconnected to CPU 12 via a signal-bearing medium. In addition, CPU 12 isconnected through communication port 18 to a communication network 20,having an attached plurality of additional computer host systems 24. Inaddition, memory device 16 and the CPU 12 may be embedded and includedin each component of the computing system 10. Each storage system mayalso include separate and/or distinct memory devices 16 and CPU 12 thatwork in conjunction or as a separate memory device 16 and/or CPU 12.

FIG. 2 is an exemplary block diagram 200 showing a hardware structure ofa data storage system in a computer system according to the presentinvention. Host computers 210, 220, 225, are shown, each acting as acentral processing unit for performing data processing as part of a datastorage system 200. The cluster hosts/nodes (physical or virtualdevices), 210, 220, and 225 may be one or more new physical devices orlogical devices to accomplish the purposes of the present invention inthe data storage system 200. In one embodiment, by way of example only,a data storage system 200 may be implemented as IBM® ProtecTIER®deduplication system TS7650G™. A Network connection 260 may be a fibrechannel fabric, a fibre channel point to point link, a fibre channelover ethernet fabric or point to point link, a FICON or ESCON I/Ointerface, any other I/O interface type, a wireless network, a wirednetwork, a LAN, a WAN, heterogeneous, homogeneous, public (i.e. theInternet), private, or any combination thereof. The hosts, 210, 220, and225 may be local or distributed among one or more locations and may beequipped with any type of fabric (or fabric channel) (not shown in FIG.2) or network adapter 260 to the storage controller 240, such as Fibrechannel, FICON, ESCON, Ethernet, fiber optic, wireless, or coaxialadapters. Data storage system 200 is accordingly equipped with asuitable fabric (not shown in FIG. 2) or network adaptor 260 tocommunicate. Data storage system 200 is depicted in FIG. 2 comprisingstorage controllers 240 and cluster hosts 210, 220, and 225. The clusterhosts 210, 220, and 225 may include cluster nodes.

To facilitate a clearer understanding of the methods described herein,storage controller 240 is shown in FIG. 2 as a single processing unit,including a microprocessor 242, system memory 243 and nonvolatilestorage (“NVS”) 216. It is noted that in some embodiments, storagecontroller 240 is comprised of multiple processing units, each withtheir own processor complex and system memory, and interconnected by adedicated network within data storage system 200. Storage 230 (labeledas 230 a, 230 b, and 230 n in FIG. 3) may be comprised of one or morestorage devices, such as storage arrays, which are connected to storagecontroller 240 (by a storage network) with one or more cluster hosts210, 220, and 225 connected to each storage controller 240.

In some embodiments, the devices included in storage 230 may beconnected in a loop architecture. Storage controller 240 manages storage230 and facilitates the processing of write and read requests intendedfor storage 230. The system memory 243 of storage controller 240 storesprogram instructions and data, which the processor 242 may access forexecuting functions and method steps of the present invention forexecuting and managing storage 230 as described herein. In oneembodiment, system memory 243 includes, is in association with, or is incommunication with the operation software 250 for performing methods andoperations described herein. As shown in FIG. 2, system memory 243 mayalso include or be in communication with a cache 245 for storage 230,also referred to herein as a “cache memory”, for buffering “write data”and “read data”, which respectively refer to write/read requests andtheir associated data. In one embodiment, cache 245 is allocated in adevice external to system memory 243, yet remains accessible bymicroprocessor 242 and may serve to provide additional security againstdata loss, in addition to carrying out the operations as described inherein.

In some embodiments, cache 245 is implemented with a volatile memory andnon-volatile memory and coupled to microprocessor 242 via a local bus(not shown in FIG. 2) for enhanced performance of data storage system200. The NVS 216 included in data storage controller is accessible bymicroprocessor 242 and serves to provide additional support foroperations and execution of the present invention as described in otherfigures. The NVS 216, may also referred to as a “persistent” cache, or“cache memory” and is implemented with nonvolatile memory that may ormay not utilize external power to retain data stored therein. The NVSmay be stored in and with the cache 245 for any purposes suited toaccomplish the objectives of the present invention. In some embodiments,a backup power source (not shown in FIG. 2), such as a battery, suppliesNVS 216 with sufficient power to retain the data stored therein in caseof power loss to data storage system 200. In certain embodiments, thecapacity of NVS 216 is less than or equal to the total capacity of cache245.

Storage 230 may be physically comprised of one or more storage devices,such as storage arrays. A storage array is a logical grouping ofindividual storage devices, such as a hard disk. In certain embodiments,storage 230 is comprised of a JBOD (Just a Bunch of Disks) array or aRAID (Redundant Array of Independent Disks) array. A collection ofphysical storage arrays may be further combined to form a rank, whichdissociates the physical storage from the logical configuration. Thestorage space in a rank may be allocated into logical volumes, whichdefine the storage location specified in a write/read request.

In one embodiment, by way of example only, the storage system as shownin FIG. 2 may include a logical volume, or simply “volume,” may havedifferent kinds of allocations. Storage 230 a, 230 b and 230 n are shownas ranks in data storage system 200, and are referred to herein as rank230 a, 230 b and 230 n. Ranks may be local to data storage system 200,or may be located at a physically remote location. In other words, alocal storage controller may connect with a remote storage controllerand manage storage at the remote location. Rank 230 a is shownconfigured with two entire volumes, 234 and 236, as well as one partialvolume 232 a. Rank 230 b is shown with another partial volume 232 b.Thus volume 232 is allocated across ranks 230 a and 230 b. Rank 230 n isshown as being fully allocated to volume 238—that is, rank 230 n refersto the entire physical storage for volume 238. From the above examples,it will be appreciated that a rank may be configured to include one ormore partial and/or entire volumes. Volumes and ranks may further bedivided into so-called “tracks,” which represent a fixed block ofstorage. A track is therefore associated with a given volume and may begiven a given rank.

The storage controller 240 may include a data duplication module 255, asimilarity index module 257 (e.g., a similarity search structure), and asimilarity search module 259. The data duplication module 255, thesimilarity index module 257, and the similarity search module 259 maywork in conjunction with each and every component of the storagecontroller 240, the hosts 210, 220, 225, and storage devices 230. Thedata duplication module 255, the similarity index module 257, and thesimilarity search module 259 may be structurally one complete module ormay be associated and/or included with other individual modules. Thedata duplication module 255, the similarity index module 257, and thesimilarity search module 259 may also be located in the cache 245 orother components.

The storage controller 240 includes a control switch 241 for controllingthe fiber channel protocol to the host computers 210, 220, 225, amicroprocessor 242 for controlling all the storage controller 240, anonvolatile control memory 243 for storing a microprogram (operationsoftware) 250 for controlling the operation of storage controller 240,data for control, cache 245 for temporarily storing (buffering) data,and buffers 244 for assisting the cache 245 to read and write data, acontrol switch 241 for controlling a protocol to control data transferto or from the storage devices 230, the data duplication module 255, thesimilarity index module 257, and the similarity search module 259, inwhich information may be set. Multiple buffers 244 may be implementedwith the present invention to assist with the operations as describedherein. In one embodiment, the cluster hosts/nodes, 210, 220, 225 andthe storage controller 240 are connected through a network adaptor (thiscould be a fibre channel) 260 as an interface i.e., via at least oneswitch called “fabric.”

In one embodiment, the host computers or one or more physical or virtualdevices, 210, 220, 225 and the storage controller 240 are connectedthrough a network (this could be a fibre channel) 260 as an interfacei.e., via at least one switch called “fabric.” In one embodiment, theoperation of the system shown in FIG. 2 will be described. Themicroprocessor 242 may control the memory 243 to store commandinformation from the host device (physical or virtual) 210 andinformation for identifying the host device (physical or virtual) 210.The control switch 241, the buffers 244, the cache 245, the operatingsoftware 250, the microprocessor 242, memory 243, NVS 216, dataduplication module 255, the similarity index module 257, and thesimilarity search module 259 are in communication with each other andmay be separate or one individual component(s). Also, several, if notall of the components, such as the operation software 250 may beincluded with the memory 243. Each of the components within the devicesshown may be linked together and may be in communication with each otherfor purposes suited to the present invention. As mentioned above, thedata duplication module 255, the similarity index module 257, and thesimilarity search module 259 may also be located in the cache 245 orother components. As such, the data duplication module 255, thesimilarity index module 257, and the similarity search module 259 maybeused as needed, based upon the storage architecture and userspreferences.

As mentioned above, in one embodiment, the input data is partitionedinto large fixed size chunks (e.g. 16 MB), and a similarity searchprocedure is applied for each input chunk. A similarity search procedurecalculates compact similarity elements, based on the input chunk ofdata, and searches for matching similarity elements stored in a compactsearch structure (i.e. index) in the repository. The size of thesimilarity elements stored per each chunk of data is typically 32 bytes(where the chunk size is a few megabytes), thus making the searchstructure storing the similarity elements very compact and simple tomaintain and search within.

The similarity elements are calculated by calculating rolling hashvalues on the chunk's data, namely producing a rolling hash value foreach consecutive window of bytes in a byte offset, and then selectingspecific hash values and associated positions (not necessarily the exactpositions of these hash values) to be the similarity elements of thechunk.

One important aspect and novelty provided by the present invention isthat a single linear calculation of rolling hash values, which is acomputationally expensive operation, serves as basis for calculatingboth the similarity elements of a chunk (for a similarity search) andthe segmentation of the chunk's data into digest blocks (for findingexact matches). Each rolling hash value is added to the calculation ofthe similarity elements as well as to the calculation of the digestblocks segmentation. After being added to the two calculations, arolling hash value can be discarded, as the need to store the rollinghash values is minimized or eliminated. This algorithmic elementprovides significant efficiency and reduction of CPU consumption, aswell as considerable performance improvement.

In one embodiment, the similarity search procedure of the presentinvention produces two types of output. The first type of output is aset of positions of the most similar reference data in the repository.The second type of output is the digests of the input chunk, comprisingof the segmentation to digest blocks and the digest values correspondingto the digest blocks, where the digest values are calculated based onthe data of the digest blocks.

In one embodiment, the digests are stored in the repository in a formthat corresponds to the digests occurrence in the data. Given a positionin the repository and size of a section of data, the location in therepository of the digests corresponding to that interval of data isefficiently determined. The positions produced by the similarity searchprocedure are then used to lookup the stored digests of the similarreference data, and to load these reference digests into memory. Then,rather than comparing data, the input digests and the loaded referencedigests are matched. The matching process is performed by loading thereference digests into a compact search structure of digests in memory,and then for each input digest, querying the search structure of digestsfor existence of that digest value. Search in the search structure ofdigests is performed based on the digest values. If a match is found,then the input data associated with that digest is determined to befound in the repository and the position of the input data in therepository is determined based on the reference digest's position in therepository. In this case, the identity between the input data covered bythe input digest, and the repository data covered by the matchingreference digest, is recorded. If a match is not found then the inputdata associated with that digest is determined to be not found in therepository, and is recorded as new data. In one embodiment, thesimilarity search structure is a global search structure of similarityelements, and a memory search structure of digests' is a local searchstructure of digests in memory. The search in the memory searchstructure of digests is performed by the digest values.

FIG. 3 is a flowchart illustrating an exemplary method 300 for digestretrieval based on similarity search in deduplication processing in adata deduplication system in which aspects of the present invention maybe realized. The method 300 begins (step 302). The method 300 partitionsinput data into data chunks (step 304). The input data may bepartitioned into fixed sized data chunks. The method 300 calculates, foreach of the data chunks, similarity elements, digest block boundaries,and corresponding digest values are calculated (step 306). The method300 searches for matching similarity elements in a search structure(i.e. index) for each of the data chunks (which may be fixed size datachunks) (step 308). The positions of the similar data in a repository(e.g., a repository of data) are located (step 310). The method 300 usesthe positions of the similar data to locate and load into memory storeddigest values and corresponding stored digest block boundaries of thesimilar data in the repository (step 312). The method 300 matches thedigest values and the corresponding digest block boundaries of the inputdata with the stored digest values and the corresponding stored digestblock boundaries to find data matches (step 314). The method 300 ends(step 316).

FIG. 4 is a flowchart illustrating an exemplary alternative method 400for digest retrieval based on similarity search in deduplicationprocessing in a data deduplication system in which aspects of thepresent invention may be realized. The method 400 begins (step 402). Themethod 400 partitions the input data into chunks (e.g., partitions theinput data into large fixed size chunks) (step 404), and for an inputdata chunk calculates rolling hash values, similarity elements, digestblock boundaries, and digest values based on data of the input datachunk (step 406). The method 400 searches for similarity elements of theinput data chunk in a similarity search structure (i.e. index) (step 408and 410). The method 400 determines if there are enough or a sufficientamount of matching similarity elements (step 412). If not enoughmatching similarity elements are found then the method 400 determinesthat no similar data is found in the repository for the input datachunk, and the data of the input chunk is stored in a repository (step414) and then the method 400 ends (step 438). If enough similarityelements are found, then for each similar data interval found in arepository, the method 400 determines the position and size of eachsimilar data interval in the repository (step 416). The method 400locates the digests representing the similar data interval in therepository (step 418). The method 400 loads these digests into a searchdata structure of digests in memory (step 420). The method 400determines if there are any additional similar data intervals (step422). If yes, the method 400 returns to step 416. If no, the method 400considers each digest of the input data chunk (step 424). The method 400determines if the digest value exists in the memory search structure ofdigests (step 426). If yes, the method 400 records the identity betweenthe input data covered by the digest and the repository data having thematching digest value (step 428). If no, the method 400 records that theinput data covered by the digest is not found in the repository (step430). From both steps 428 and 430, the method 400 determines if thereare additional digests of the input data chunk (step 432). If yes, themethod 400 returns to step 424. If no, method 400 removes the similarityelements of the matched data in the repository from the similaritysearch structure (step 434 and step 410). The method 400 adds thesimilarity elements of the input data chunk to the similarity searchstructure (step 436). The method 400 ends (step 438).

FIG. 5 is a flowchart illustrating an exemplary method 500 for efficientcalculation of both similarity search values and boundaries of digestblocks using a single linear calculation of rolling hash values in adata deduplication system in which aspects of the present invention maybe realized. The method 500 begins (step 502). The method 500 partitionsinput data into data chunks (steps 504). The data chunks may be fixedsized data chunks. The method 500 considers each consecutive window ofbytes in a byte offset in the input data (step 506). The method 500determines if there is an additional consecutive window of bytes to beprocessed (step 508). If yes, the method 500 calculates a rolling hashvalue based on the data of the consecutive window of bytes (step 510).The method 500 contributes the rolling hash value to the calculation ofthe similarity values and to the calculation of the digest blockssegmentations (i.e., the digest block boundaries) (step 512). The method500 discards the rolling hash value (step 514), and returns to step 506.If no, the method 500 concludes the calculation of the similarityelements and of the digest blocks segmentation, producing the finalsimilarity elements and digest blocks segmentation of the input data(step 516). The method 500 calculates digest values based on the digestblocks segmentation, wherein each digest block is assigned with acorresponding digest value (step 518). The similarity elements are usedto search for similar data in the repository (step 520). The digestblocks and corresponding digest values are used for matching with digestblocks and corresponding digest values stored in a repository fordetermining data in the repository which is identical to the input data(step 522). The method 500 ends (step 524).

FIG. 6 is a block diagram illustrating calculating rolling hash valuesin which aspects of the present invention may be realized. A segmentingprocess starts with a calculation of rolling hash values (shown as H inFIG. 6 as H_(i), H_(i+1) with i being an offset of a seed) of the inputdata 602, of data size n, producing a hash value H at each byte positionb (shown as b in FIG. 6 as b_(i), b_(i+m), b_(i+m), b_(i+m−1) with mbeing the seed size of m bytes), as shown below in FIG. 6. As shown,H_(i) is calculated for the seed starting at offset i based on thefunction f(b_(i) . . . b_(i+1)) and requires 0(m) operations. As shown,H_(i+1) can be calculated for the seed starting at offset i+1 based onthe function f(b_(i+1) . . . b_(i+m)), thus requiring 0(m) operations.This operation does not constitute a rolling hash calculation.Alternatively, H_(i+1) can be calculated for the seed starting at offseti+1 based on the function g(H_(i), b_(i+m), b_(i), b_(i)), thusrequiring 0(3) operations, which is considerably more efficient than thefirst alternative. This operation is a rolling hash calculation.

As mentioned above, there is a trade-off with regards to the size of thewindow of bytes based on which the rolling hash values are calculated.The rolling hash values are calculated based on a sliding window ofbytes. If this sliding window is small, the rolling hash values arebased on fewer bytes thus constituting a less reliable random variable,and also reducing the statistical significance of segmentingcalculations which are based on these values. If the sliding window islarge, then each rolling hash value is more sensitive to changes in thedata (a given change in the data affects more hash values), thus makingthe segmenting positions more sensitive to changes in the data, asdepicted below in FIG. 7, thus allowing minor or otherwise localizedchanges in the input data to alter the segmentation in an undesirableway. In other words, whether the window size is increased or decreased,both directions have a negative effect on the quality of the blocksegmentation.

FIG. 7 is a block diagram illustrating the effect of a data modificationwithin a sliding window that determined a segment boundary in a previousgeneration of the data in which aspects of the present invention may berealized. FIG. 7 is a block diagram illustrating the finding ofadditional identical data with the secondary segmentation. FIG. 7 showsan original generation of data 702A, and a new generation of the data704A, where in the new generation of the data 704A a byte was changed inthe position 714, relative to the original generation of the data 702A.FIG. 7 further shows the original generation of the data in a segmentedform 702B, and the new generation of the data in a segmented form 704B.In the original generation of the data in a segmented form 702B theposition of the modified byte 714 is located within a sliding windowthat starts at a position marked as 706. Since the byte changed, in thenew generation of the data in a segmented form 704B the sliding windowstarting at the same position does not yield a segmenting position, andhence there is no segment boundary generated at the position marked by710. The data in the segment 712 did not change, but as shown in thefigure the last segment in the new generation of data includes twoseparate segments in the original generation of data. This differencecauses a difference in the calculated digest values and thus reductionin deduplication. FIG. 7 shows the start positions of the slidingwindows used to declare segment boundaries 706 in both generations ofthe data. Also, the segment boundaries are declared at the end of thesliding windows 708.

FIG. 8 is a block diagram illustrating a method for finding additionalidentical data using a secondary segmentation in which aspects of thepresent invention may be realized. In the interval of new data 804C, twosegment boundaries 808 are found (the segment boundaries are determinedat the end positions of sliding windows that produced a segmenting hashvalue). The segment of data 816 is determined as a mismatch relative toexisting data when searching hashes of segments produced by thesegmentation calculated based on the new data. The data in the segment812 did not change however, but as shown in the FIG. 8, segment 812 isnot identified as unchanged data relative to existing data.

For the same interval of new data, referred to as 804D, with the samesegment boundaries 808, a new segment boundary 820 is derived from theboundaries of the original data in a repository of data, and the newboundary 820 is applied to the interval of data 804D. With the newboundary 820, the hash value of segment 812 can be found in the hashvalues of the original data, to identify that there is no change in thedata of segment 812 relative to the original data. Therefore, the newmismatch, after applying the segment boundary 820, is the mismatchdepicted as 818. It can be observed that mismatch 818 is smaller thanmismatch 816, thus reducing the mismatch size and increasing theeffectiveness of deduplication using a secondary segmentation derivedfrom original repository data.

FIG. 9 is a flowchart illustrating an exemplary method 900 for producingand using secondary segmentations derived from references in a datadeduplication system in which aspects of the present invention may berealized. The method 900 begins (step 902). The method 900 receives aninput data chunk (step 904). The method 900 segments the data using adata-based segmenting technique (step 906). Digest values are generatedin accordance with the segments (step 908). The method 900 searches theinput digest values in a search structure of reference digest values(step 910). The method 900 creates matches and mismatches based on theinput digest values that were found and not found (step 912). The method900 determines if there additional mismatches to process (step 914). Ifno, the method ends (step 926). If yes, the method 900 searches the Hvand Tv digests covered by the matches immediately preceding andfollowing the mismatch, in a digests search structure, producing Hr andTr matching reference digests, respectively (step 916). The method 900projects the following Hs and preceding Ts reference digest segments bytheir positions and sizes onto the input data, producing new secondaryinput digest segments (step 918). For each of the new secondary digestsegments, the method 900 calculates respective digest values based onthe input data (step 920). The method 900 searches for the new secondarydigests in a search structure of reference digests (step 922). For eachnew match found, an appropriate data identity is recorded thus reducingthe mismatch size (step 924). The method 900 returns to step 914.

As will be appreciated by one skilled in the art, aspects of the presentinvention may be embodied as a system, method or computer programproduct. Accordingly, aspects of the present invention may take the formof an entirely hardware embodiment, an entirely software embodiment(including firmware, resident software, micro-code, etc.) or anembodiment combining software and hardware aspects that may allgenerally be referred to herein as a “circuit,” “module” or “system.”Furthermore, aspects of the present invention may take the form of acomputer program product embodied in one or more computer readablemedium(s) having computer readable program code embodied thereon.

Any combination of one or more computer readable medium(s) may beutilized. The computer readable medium may be a computer readable signalmedium or a computer readable storage medium. A computer readablestorage medium may be, for example, but not limited to, an electronic,magnetic, optical, electromagnetic, infrared, or semiconductor system,apparatus, or device, or any suitable combination of the foregoing. Morespecific examples (a non-exhaustive list) of the computer readablestorage medium would include the following: an electrical connectionhaving one or more wires, a portable computer diskette, a hard disk, arandom access memory (RAM), a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM or Flash memory), an optical fiber,a portable compact disc read-only memory (CD-ROM), an optical storagedevice, a magnetic storage device, or any suitable combination of theforegoing. In the context of this document, a computer readable storagemedium may be any tangible medium that may contain, or store a programfor use by or in connection with an instruction execution system,apparatus, or device.

Program code embodied on a computer readable medium may be transmittedusing any appropriate medium, including but not limited to wireless,wired, optical fiber cable, RF, etc., or any suitable combination of theforegoing. Computer program code for carrying out operations for aspectsof the present invention may be written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Java, Smalltalk, C++ or the like and conventionalprocedural programming languages, such as the “C” programming languageor similar programming languages. The program code may execute entirelyon the user's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

Aspects of the present invention have been described above withreference to flowchart illustrations and/or block diagrams of methods,apparatus (systems) and computer program products according toembodiments of the invention. It will be understood that each block ofthe flowchart illustrations and/or block diagrams, and combinations ofblocks in the flowchart illustrations and/or block diagrams, may beimplemented by computer program instructions. These computer programinstructions may be provided to a processor of a general purposecomputer, special purpose computer, or other programmable dataprocessing apparatus to produce a machine, such that the instructions,which execute via the processor of the computer or other programmabledata processing apparatus, create means for implementing thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

These computer program instructions may also be stored in a computerreadable medium that may direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the flowchart and/or blockdiagram block or blocks. The computer program instructions may also beloaded onto a computer, other programmable data processing apparatus, orother devices to cause a series of operational steps to be performed onthe computer, other programmable apparatus or other devices to produce acomputer implemented process such that the instructions which execute onthe computer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

The flowchart and block diagrams in the above figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof code, which comprises one or more executable instructions forimplementing the specified logical function(s). It should also be notedthat, in some alternative implementations, the functions noted in theblock may occur out of the order noted in the figures. For example, twoblocks shown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved. It will also be notedthat each block of the block diagrams and/or flowchart illustration, andcombinations of blocks in the block diagrams and/or flowchartillustration, may be implemented by special purpose hardware-basedsystems that perform the specified functions or acts, or combinations ofspecial purpose hardware and computer instructions.

What is claimed is:
 1. A method for producing a plurality ofsegmentations of input data into blocks in a data deduplication systemusing a processor device in a computing environment, comprising:calculating digests for an input data chunk using a primarysegmentation; obtaining and applying secondary segmentations for eachone of a plurality of data mismatches based on reference data; andstoring the primary segmentation and corresponding primary digests forthe input data chunk.
 2. The method of claim 1, further includingproducing data matches and the data mismatches by searching inputdigests in a search structure of reference digests.
 3. The method ofclaim 1, further including obtaining the segmentations for each one ofthe data mismatches by considering input digests included in datamatches preceding and following each one of the data mismatches.
 4. Themethod of claim 3, further including matching the considered inputdigests with reference digests to produce alternative digest matches. 5.The method of claim 4, further including defining the alternativedigests matches to serve as starting positions for the secondarysegmentations which are projected onto the input data.
 6. The method ofclaim 5, further including one of: calculating new digest values for theinput data based on the secondary segmentations; and searching newdigest values in a search structure of reference digest values toproduce new digests matches.
 7. The method of claim 6, further includinggenerating new data matches corresponding to the produced new digestsmatches
 8. The method of claim 1, further including avoiding storing thesecondary segmentations and corresponding secondary digests for theinput data chunk.
 9. A system for producing a plurality of segmentationsof input data into blocks in a data deduplication system of a computingenvironment, the system comprising: the data deduplication system; arepository operating in the data deduplication system; a memory in thedata deduplication system; a search structure in association with thememory in the data deduplication system; and at least one processordevice operable in the computing storage environment for controlling thedata deduplication system, wherein the at least one processor device:calculates digests for an input data chunk using a primary segmentation,obtains and applies secondary segmentations for each one of a pluralityof data mismatches based on reference data, and stores the primarysegmentation and corresponding primary digests for the input data chunk.10. The system of claim 9, wherein the at least one processor deviceproduces data matches and the data mismatches by searching input digestsin a search structure of reference digests.
 11. The system of claim 9,wherein the at least one processor device obtains the segmentations foreach one of the data mismatches by considering input digests included indata matches preceding and following each one of the data mismatches.12. The system of claim 11, wherein the at least one processor devicematches the considered input digests with reference digests to producealternative digest matches.
 13. The system of claim 12, wherein the atleast one processor device defines the alternative digests matches toserve as starting positions for the secondary segmentations which areprojected onto the input data.
 14. The system of claim 13, wherein theat least one processor device performs one of: calculating new digestvalues for the input data based on the secondary segmentations, andsearching new digest values in a search structure of reference digestvalues to produce new digests matches.
 15. The system of claim 14,wherein the at least one processor device generates new data matchescorresponding to the produced new digests matches.
 16. The system ofclaim 15, wherein the at least one processor device avoids storing thesecondary segmentations and corresponding secondary digests for theinput data chunk.
 17. A computer program product for producing aplurality of segmentations of input data into blocks in a datadeduplication system using a processor device in a computingenvironment, the computer program product comprising a computer-readablestorage medium having computer-readable program code portions storedtherein, the computer-readable program code portions comprising: a firstexecutable portion that calculates digests for an input data chunk usinga primary segmentation; a second executable portion that obtains andapplies secondary segmentations for each one of a plurality of datamismatches based on reference data; and a third executable portion thatstores the primary segmentation and corresponding primary digests forthe input data chunk.
 18. The computer program product of claim 17,further including a fourth executable portion that produces data matchesand the data mismatches by searching input digests in a search structureof reference digests.
 18. The computer program product of claim 17,further including a fourth executable portion that obtains thesegmentations for each one of the data mismatches by considering inputdigests included in data matches preceding and following each one of thedata mismatches.
 19. The computer program product of claim 18, furtherincluding a fifth executable portion that matches the considered inputdigests with reference digests to produce alternative digest matches.20. The computer program product of claim 19, further including a sixthexecutable portion that defines the alternative digests matches to serveas starting positions for the secondary segmentations which areprojected onto the input data.
 21. The computer program product of claim20, further including a seventh executable portion that performs one of:calculating new digest values for the input data based on the secondarysegmentations, and searching new digest values in a search structure ofreference digest values to produce new digests matches.
 22. The computerprogram product of claim 21, further including an eighth executableportion that generates new data matches corresponding to the producednew digests matches.
 24. The computer program product of claim 23,further including a ninth executable portion that avoids storing thesecondary segmentations and corresponding secondary digests for theinput data chunk.